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Poplar Optimization Algorithm || Step-By-Step || ~xRay Pixy

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The Poplar Optimization Algorithm (POA) is a nature-inspired optimization method based on how poplar trees reproduce. It uses sexual propagation (seed dispersal by wind) for exploration and asexual reproduction (cutting and regrowth) for exploitation. Mutation and chaos factors help maintain diversity and prevent premature convergence, making POA efficient for solving complex optimization problems. Learn the Poplar Optimization Algorithm Step-By-Step using Examples. Video Chapters: Poplar Optimization Algorithm (POA) 00:00 Introduction 02:12 POA Applications 03:32 POA Steps 05:50 Execute Algorithm 1 13:45 Execute Algorithm 2 16:38 Execute Algorithm 3 18:15 Conclusion Main Points of the Poplar Optimization Algorithm (POA) Nature-Inspired Algorithm – Based on the reproductive mechanisms of poplar trees. Two Key Processes : Sexual Propagation (Seed Dispersal) – Uses wind to spread seeds, allowing broad exploration. Asexual Reproduction (Cuttings) – Strong branches grow ...
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Squirrel Search Algorithm (SSA) || STEP - BY - STEP || ~xRay Pixy

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Squirrel Search Algorithm (SSA) Learn Squirrel Search Optimization Algorithm Step-By-Step with Example. Video Chapters: Introduction: 00:00 Squirrel Search Algorithm: 01:11 Squirrel Search Algorithm MODEL: 03:33 Squirrel Search Algorithm STEPS: 06:18 Squirrel Search Algorithm MATHEMATICAL MODELS: 06:26 Conclusion: 15:37
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Optimal Wind Turbine Placement Using Particle Swarm Optimization

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Wind Turbine Optimal Positioning using Particle Swarm Optimization Algorithm Video Chapters: Introduction: 00:00 Wind Energy Projects Objectives: 01:15 Wind Turbine: 04:16 Wind Farm: 05:20 Jensen Wake Effect Model: 06:55 Wind Farm Layout: 09:05 3 Scenarios for Optimal Wind Turbine Positions: 12:02 Metaheuristics for Wind Energy Optimization: 13:54 Optimal Wind Turbine Placement Using Particle Swarm Optimization: 14:53 Optimization Process Flowchart: 20:08 Conclusion: 21:00
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All Members-Based Optimizer (AMBO) || STEP-BY-STEP || ~xRay Pixy

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All Members-Based Optimizer (AMBO) Learn All Members-Based Optimizer Step-by-Step with Examples. Algorithm Type: Metaheuristic Optimization Technique Algorithm Main Idea: Make more use of the Population Matrix. Tested on Different Benchmark Test Functions. Algorithm Performance: Provide Better results in comparison with different metaheuristic optimization algorithms. Used for Solving Optimization Problems. ALGORITHM MAIN IDEA Make use of the Population Matrix and All Members can play role in Updating Algorithm Population. ALL MEMBERS-BASED OPTIMIZER STEPS STEP 01: Initialize Algorithm Important Parameters. STEP 02: Initialize Population Randomly in the Search Space. STEP 03: Evaluate Initial Population using the Fitness Function. STEP 04: Check While (Current Iteration < Maximum Iteration) Do STEP 05: Update Members Position and Best Member Position. STEP 06: Update Population Members using STAGE 01. STEP 07: Update Population Members using STAGE 02. STEP 08: Save Best Solut...
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Elephant Herding Optimization Algorithm || STEP-BY-STEP || ~xRay Pixy

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Elephant Herding Optimization Algorithm Learn Elephant Herding Optimization Algorithm Step-By-Step with Examples. Elephant Herding Optimization Algorithm - Introduced in 2015 - Inspired by Elephant Herding Behavior. - Main Operator used: + Elephant Clan Updating Operator + Elephant Separating Operator - Used to Solve Optimization Problems.
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